Simple food over representation analysis (ORA)

Pol Castellano-Escuder

Duke University

6 May 2024

Source: vignettes/food_enrichment_analysis.Rmd

Compiled date: 2024-05-06

Last edited: 2022-01-12

License: GPL-3

Installation

Run the following code to install the Bioconductor version of the package.

# install.packages("BiocManager")
BiocManager::install("fobitools")

Load fobitools

You can also load some additional packages that will be very useful in this vignette.

metaboliteUniverse and metaboliteList

In microarrays, for example, we can study almost all the genes of an organism in our sample, so it makes sense to perform an over representation analysis (ORA) considering all the genes present in Gene Ontology (GO). Since most of the GO pathways would be represented by some gene in the microarray.

This is different in nutrimetabolomics. Targeted nutrimetabolomics studies sets of about 200-500 diet-related metabolites, so it would not make sense to use all known metabolites (for example in HMDB or CHEBI) in an ORA, as most of them would not have been quantified in the study.

In nutrimetabolomic studies it may be interesting to study enriched or over represented foods/food groups by the metabolites resulting from the study statistical analysis, rather than the enriched metabolic pathways, as would make more sense in genomics or other metabolomics studies.

The Food-Biomarker Ontology (FOBI) provides a biological knowledge for conducting these enrichment analyses in nutrimetabolomic studies, as FOBI provides the relationships between several foods and their associated dietary metabolites (Castellano-Escuder et al. 2020).

Accordingly, to perform an ORA with the fobitools package, it is necessary to provide a metabolite universe (all metabolites included in the statistical analysis) and a list of selected metabolites (selected metabolites according to a statistical criterion).

Here is an example:

# select 300 random metabolites from FOBI
idx_universe <- sample(nrow(fobitools::idmap), 300, replace = FALSE)
metaboliteUniverse <- fobitools::idmap %>%
  dplyr::slice(idx_universe) %>%
  pull(FOBI)

# select 10 random metabolites from metaboliteUniverse that are associated with 'Red meat' (FOBI:0193), 
# 'Lean meat' (FOBI:0185) , 'egg food product' (FOODON:00001274), 
# or 'grape (whole, raw)' (FOODON:03301702)
fobi_subset <- fobitools::fobi %>% # equivalent to `parse_fobi()`
  filter(FOBI %in% metaboliteUniverse) %>%
  filter(id_BiomarkerOf %in% c("FOBI:0193", "FOBI:0185", "FOODON:00001274", "FOODON:03301702")) %>%
  dplyr::slice(sample(nrow(.), 10, replace = FALSE))

metaboliteList <- fobi_subset %>%
  pull(FOBI)
fobitools::ora(metaboliteList = metaboliteList, 
               metaboliteUniverse = metaboliteUniverse, 
               subOntology = "food", 
               pvalCutoff = 0.01)
className classSize overlap pval padj overlapMetabolites
Red meat 9 5 0.0000008 0.0001084 FOBI:030687, FOBI:030708, FOBI:030406, FOBI:030689, FOBI:050243
grapefruit (whole, raw) 14 5 0.0000116 0.0008132 FOBI:030406, FOBI:030524, FOBI:050243, FOBI:050258, FOBI:050287
Lean meat 3 3 0.0000189 0.0008799 FOBI:030687, FOBI:030708, FOBI:030689
apple juice 9 4 0.0000448 0.0010459 FOBI:030406, FOBI:050243, FOBI:050258, FOBI:050287
lemon (whole, raw) 9 4 0.0000448 0.0010459 FOBI:030406, FOBI:050243, FOBI:050258, FOBI:050287
orange juice 9 4 0.0000448 0.0010459 FOBI:030406, FOBI:050243, FOBI:050258, FOBI:050287
orange (whole, raw) 13 4 0.0002407 0.0043221 FOBI:030406, FOBI:050243, FOBI:050258, FOBI:050287
black coffee 2 2 0.0008027 0.0043221 FOBI:030406, FOBI:050243
black pepper food product 2 2 0.0008027 0.0043221 FOBI:030406, FOBI:050243
black tea leaf (dry) 2 2 0.0008027 0.0043221 FOBI:030406, FOBI:050243
black turtle bean (whole) 2 2 0.0008027 0.0043221 FOBI:030406, FOBI:050243
blueberry (whole, raw) 2 2 0.0008027 0.0043221 FOBI:030406, FOBI:050243
chocolate 2 2 0.0008027 0.0043221 FOBI:030406, FOBI:050243
green tea leaf (dry) 2 2 0.0008027 0.0043221 FOBI:030406, FOBI:050243
kale leaf (raw) 2 2 0.0008027 0.0043221 FOBI:030406, FOBI:050243
pea (whole) 2 2 0.0008027 0.0043221 FOBI:030406, FOBI:050243
pomegranate (whole, raw) 2 2 0.0008027 0.0043221 FOBI:030406, FOBI:050243
prune food product 2 2 0.0008027 0.0043221 FOBI:030406, FOBI:050243
red tea 2 2 0.0008027 0.0043221 FOBI:030406, FOBI:050243
red velvet 2 2 0.0008027 0.0043221 FOBI:030406, FOBI:050243
stem or spear vegetable 2 2 0.0008027 0.0043221 FOBI:030406, FOBI:050243
turnip (whole, raw) 2 2 0.0008027 0.0043221 FOBI:030406, FOBI:050243
white bread 2 2 0.0008027 0.0043221 FOBI:030406, FOBI:050243
White fish 2 2 0.0008027 0.0043221 FOBI:030406, FOBI:050243
white sugar 2 2 0.0008027 0.0043221 FOBI:030406, FOBI:050243
white wine 2 2 0.0008027 0.0043221 FOBI:030406, FOBI:050243
apricot (whole, raw) 3 2 0.0023703 0.0100559 FOBI:030406, FOBI:050243
black currant (whole, raw) 3 2 0.0023703 0.0100559 FOBI:030406, FOBI:050243
blackberry (whole, raw) 3 2 0.0023703 0.0100559 FOBI:030406, FOBI:050243
broccoli floret (whole, raw) 3 2 0.0023703 0.0100559 FOBI:050243, FOBI:050258
cauliflower (whole, raw) 3 2 0.0023703 0.0100559 FOBI:030406, FOBI:050243
peach (whole, raw) 3 2 0.0023703 0.0100559 FOBI:030406, FOBI:050243
raspberry (whole, raw) 3 2 0.0023703 0.0100559 FOBI:030406, FOBI:050243
pear (whole, raw) 4 2 0.0046664 0.0176565 FOBI:030406, FOBI:050243
plum (whole, raw) 4 2 0.0046664 0.0176565 FOBI:030406, FOBI:050243
strawberry (whole, raw) 4 2 0.0046664 0.0176565 FOBI:030406, FOBI:050243
tea food product 4 2 0.0046664 0.0176565 FOBI:050243, FOBI:050258
wine (food product) 14 3 0.0057940 0.0213463 FOBI:030406, FOBI:050243, FOBI:050258
bean (whole) 5 2 0.0076553 0.0267937 FOBI:030406, FOBI:050243
onion (whole, raw) 5 2 0.0076553 0.0267937 FOBI:050243, FOBI:050258

Network visualization of metaboliteList terms

Then, with the fobi_graph function we can visualize the metaboliteList terms with their corresponding FOBI relationships.

terms <- fobi_subset %>%
  pull(id_code)

# create the associated graph
fobitools::fobi_graph(terms = terms, 
                      get = "anc",
                      labels = TRUE,
                      legend = TRUE)

Session Information 

sessionInfo()
#> R version 4.3.2 (2023-10-31)
#> Platform: aarch64-apple-darwin20 (64-bit)
#> Running under: macOS Sonoma 14.1
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
#> 
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> time zone: America/New_York
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] kableExtra_1.3.4 dplyr_1.1.4      fobitools_1.11.2 BiocStyle_2.30.0
#> 
#> loaded via a namespace (and not attached):
#>   [1] DBI_1.2.0              ada_2.0-5              qdapRegex_0.7.8       
#>   [4] gridExtra_2.3          rlang_1.1.3            magrittr_2.0.3        
#>   [7] e1071_1.7-14           compiler_4.3.2         RSQLite_2.3.4         
#>  [10] systemfonts_1.0.5      vctrs_0.6.5            rvest_1.0.3           
#>  [13] stringr_1.5.1          pkgconfig_2.0.3        crayon_1.5.2          
#>  [16] fastmap_1.1.1          labeling_0.4.3         ggraph_2.1.0          
#>  [19] utf8_1.2.4             rmarkdown_2.25         tzdb_0.4.0            
#>  [22] prodlim_2023.08.28     ragg_1.2.7             purrr_1.0.2           
#>  [25] bit_4.0.5              xfun_0.41              cachem_1.0.8          
#>  [28] jsonlite_1.8.8         blob_1.2.4             highr_0.10            
#>  [31] tictoc_1.2             BiocParallel_1.36.0    tweenr_2.0.2          
#>  [34] parallel_4.3.2         R6_2.5.1               bslib_0.6.1           
#>  [37] stringi_1.8.3          textclean_0.9.3        parallelly_1.36.0     
#>  [40] rpart_4.1.23           jquerylib_0.1.4        Rcpp_1.0.12           
#>  [43] bookdown_0.37          knitr_1.45             future.apply_1.11.1   
#>  [46] clisymbols_1.2.0       Matrix_1.6-1           splines_4.3.2         
#>  [49] nnet_7.3-19            igraph_1.6.0           tidyselect_1.2.0      
#>  [52] rstudioapi_0.15.0      yaml_2.3.8             viridis_0.6.4         
#>  [55] codetools_0.2-19       listenv_0.9.0          lattice_0.22-5        
#>  [58] tibble_3.2.1           withr_2.5.2            evaluate_0.23         
#>  [61] ontologyIndex_2.11     future_1.33.1          desc_1.4.3            
#>  [64] survival_3.5-7         proxy_0.4-27           polyclip_1.10-6       
#>  [67] xml2_1.3.6             pillar_1.9.0           BiocManager_1.30.22   
#>  [70] generics_0.1.3         vroom_1.6.5            ggplot2_3.4.4         
#>  [73] munsell_0.5.0          scales_1.3.0           ff_4.0.12             
#>  [76] globals_0.16.2         xtable_1.8-4           class_7.3-22          
#>  [79] glue_1.7.0             RecordLinkage_0.4-12.4 tools_4.3.2           
#>  [82] data.table_1.14.10     webshot_0.5.5          fgsea_1.28.0          
#>  [85] fs_1.6.3               graphlayouts_1.0.2     fastmatch_1.1-4       
#>  [88] tidygraph_1.3.0        cowplot_1.1.2          grid_4.3.2            
#>  [91] tidyr_1.3.0            ipred_0.9-14           colorspace_2.1-0      
#>  [94] ggforce_0.4.1          cli_3.6.2              evd_2.3-6.1           
#>  [97] textshaping_0.3.7      fansi_1.0.6            viridisLite_0.4.2     
#> [100] svglite_2.1.3          lava_1.7.3             gtable_0.3.4          
#> [103] sass_0.4.8             digest_0.6.34          ggrepel_0.9.5         
#> [106] farver_2.1.1           memoise_2.0.1          htmltools_0.5.7       
#> [109] pkgdown_2.0.7          lifecycle_1.0.4        httr_1.4.7            
#> [112] bit64_4.0.5            MASS_7.3-60

References

Castellano-Escuder, Pol, Raúl González-Domı́nguez, David S Wishart, Cristina Andrés-Lacueva, and Alex Sánchez-Pla. 2020. “FOBI: An Ontology to Represent Food Intake Data and Associate It with Metabolomic Data.” Database 2020.